
"""simple docstring"""






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	AutoencoderKL      ,SCREAMING_SNAKE_CASE_  :	CLIPTextModel      ,SCREAMING_SNAKE_CASE_  :	CLIPTokenizer      ,SCREAMING_SNAKE_CASE_  :	UNetaDConditionModel      ,SCREAMING_SNAKE_CASE_  :	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,SCREAMING_SNAKE_CASE_  :	StableDiffusionSafetyChecker      ,SCREAMING_SNAKE_CASE_  :	CLIPImageProcessor      ,)       ->	str:





				"""simple docstring"""


				super().__init__()
				self.register_modules(
				    vae=SCREAMING_SNAKE_CASE_      ,text_encoder=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,unet=SCREAMING_SNAKE_CASE_      ,scheduler=SCREAMING_SNAKE_CASE_      ,safety_checker=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,)






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, int]] = "auto"			)       ->	Dict:





				"""simple docstring"""


				if slice_size == "auto":
					# half the attention head size is usually a good trade-off between
					# speed and memory
					lowerCAmelCase												=  self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	Union[str, Any]:





				"""simple docstring"""


				self.enable_attention_slicing(SCREAMING_SNAKE_CASE_			)






			@torch.no_grad()
			def __call__(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Union[str, List[str]]      ,SCREAMING_SNAKE_CASE_  :	int = 512      ,SCREAMING_SNAKE_CASE_  :	int = 512      ,SCREAMING_SNAKE_CASE_  :	int = 50      ,SCREAMING_SNAKE_CASE_  :	float = 7.5      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, List[str]]] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = 1      ,SCREAMING_SNAKE_CASE_  :	float = 0.0      ,SCREAMING_SNAKE_CASE_  :	Optional[torch.Generator] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[torch.FloatTensor] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[str] = "pil"      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,SCREAMING_SNAKE_CASE_  :	int = 1      ,SCREAMING_SNAKE_CASE_  :	Optional[torch.FloatTensor] = None      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,)       ->	int:





				"""simple docstring"""


				if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  1
				elif isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				else:
					raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_			)}"""			)

				if height % 8 != 0 or width % 8 != 0:
					raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""			)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) or callback_steps <= 0)
				):
					raise ValueError(
					    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
					    F""" {type(SCREAMING_SNAKE_CASE_			)}."""			)

				# get prompt text embeddings
				lowerCAmelCase												=  self.tokenizer(
				    SCREAMING_SNAKE_CASE_      ,padding='''max_length'''      ,max_length=self.tokenizer.model_max_length      ,return_tensors='''pt'''      ,)
				lowerCAmelCase												=  text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					lowerCAmelCase												=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
					logger.warning(
					    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
					    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""			)
					lowerCAmelCase												=  text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
					lowerCAmelCase												=  self.text_encoder(text_input_ids.to(self.device			)			)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  text_embeddings.shape
				lowerCAmelCase												=  text_embeddings.repeat(1      ,SCREAMING_SNAKE_CASE_      ,1			)
				lowerCAmelCase												=  text_embeddings.view(bs_embed * num_images_per_prompt      ,SCREAMING_SNAKE_CASE_      ,-1			)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				lowerCAmelCase												=  guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
					lowerCAmelCase												=  42
					if negative_prompt is None:
						lowerCAmelCase												=  ['''''']
					elif type(SCREAMING_SNAKE_CASE_			) is not type(SCREAMING_SNAKE_CASE_			):
						raise TypeError(
						    F"""`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_			)} !="""
						    F""" {type(SCREAMING_SNAKE_CASE_			)}."""			)
					elif isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			):
						lowerCAmelCase												=  [negative_prompt]
					elif batch_size != len(SCREAMING_SNAKE_CASE_			):
						raise ValueError(
						    F"""`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_			)}, but `prompt`:"""
						    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
						    ''' the batch size of `prompt`.'''			)
					else:
						lowerCAmelCase												=  negative_prompt

					lowerCAmelCase												=  text_input_ids.shape[-1]
					lowerCAmelCase												=  self.tokenizer(
					    SCREAMING_SNAKE_CASE_      ,padding='''max_length'''      ,max_length=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      ,)
					lowerCAmelCase												=  self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					lowerCAmelCase												=  uncond_embeddings.shape[1]
					lowerCAmelCase												=  uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,1			)
					lowerCAmelCase												=  uncond_embeddings.view(batch_size * num_images_per_prompt      ,SCREAMING_SNAKE_CASE_      ,-1			)

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					lowerCAmelCase												=  torch.cat([uncond_embeddings, text_embeddings]			)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				lowerCAmelCase												=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				lowerCAmelCase												=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				lowerCAmelCase												=  text_embeddings.dtype
				if latents is None:
					if self.device.type == "mps":
						# randn does not exist on mps
						lowerCAmelCase												=  torch.randn(
						    SCREAMING_SNAKE_CASE_      ,generator=SCREAMING_SNAKE_CASE_      ,device='''cpu'''      ,dtype=SCREAMING_SNAKE_CASE_			).to(self.device			)
						lowerCAmelCase												=  torch.randn(SCREAMING_SNAKE_CASE_      ,generator=SCREAMING_SNAKE_CASE_      ,device='''cpu'''      ,dtype=SCREAMING_SNAKE_CASE_			).to(
						    self.device			)
					else:
						lowerCAmelCase												=  torch.randn(
						    SCREAMING_SNAKE_CASE_      ,generator=SCREAMING_SNAKE_CASE_      ,device=self.device      ,dtype=SCREAMING_SNAKE_CASE_			)
						lowerCAmelCase												=  torch.randn(SCREAMING_SNAKE_CASE_      ,generator=SCREAMING_SNAKE_CASE_      ,device=self.device      ,dtype=SCREAMING_SNAKE_CASE_			)
				else:
					if latents_reference.shape != latents_shape:
						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""			)
					lowerCAmelCase												=  latents_reference.to(self.device			)
					lowerCAmelCase												=  latents.to(self.device			)

				# This is the key part of the pipeline where we
				# try to ensure that the generated images w/ the same seed
				# but different sizes actually result in similar images
				lowerCAmelCase												=  (latents_shape[3] - latents_shape_reference[3]) // 2
				lowerCAmelCase												=  (latents_shape[2] - latents_shape_reference[2]) // 2
				lowerCAmelCase												=  latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				lowerCAmelCase												=  latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				lowerCAmelCase												=  0 if dx < 0 else dx
				lowerCAmelCase												=  0 if dy < 0 else dy
				lowerCAmelCase												=  max(-dx      ,0			)
				lowerCAmelCase												=  max(-dy      ,0			)
				# import pdb
				# pdb.set_trace()
				lowerCAmelCase												=  latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				lowerCAmelCase												=  self.scheduler.timesteps.to(self.device			)

				# scale the initial noise by the standard deviation required by the scheduler
				lowerCAmelCase												=  latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				lowerCAmelCase												=  '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
				lowerCAmelCase												=  {}
				if accepts_eta:
					lowerCAmelCase												=  eta

				for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_			)			):
					# expand the latents if we are doing classifier free guidance
					lowerCAmelCase												=  torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
					lowerCAmelCase												=  self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

					# predict the noise residual
					lowerCAmelCase												=  self.unet(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,encoder_hidden_states=SCREAMING_SNAKE_CASE_			).sample

					# perform guidance
					if do_classifier_free_guidance:
						lowerCAmelCase							,       lowerCAmelCase												=  noise_pred.chunk(2			)
						lowerCAmelCase												=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

					# compute the previous noisy sample x_t -> x_t-1
					lowerCAmelCase												=  self.scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  1 / 0.1_82_15 * latents
				lowerCAmelCase												=  self.vae.decode(SCREAMING_SNAKE_CASE_			).sample

				lowerCAmelCase												=  (image / 2 + 0.5).clamp(0      ,1			)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				lowerCAmelCase												=  image.cpu().permute(0      ,2      ,3      ,1			).float().numpy()

				if self.safety_checker is not None:
					lowerCAmelCase												=  self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)      ,return_tensors='''pt'''			).to(
					    self.device			)
					lowerCAmelCase							,       lowerCAmelCase												=  self.safety_checker(
					    images=SCREAMING_SNAKE_CASE_      ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype			)			)
				else:
					lowerCAmelCase												=  None

				if output_type == "pil":
					lowerCAmelCase												=  self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

				if not return_dict:
					return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_      ,nsfw_content_detected=SCREAMING_SNAKE_CASE_			)




"""simple docstring"""






UpperCAmelCase        =				8.3_144_598




def __magic_name__	(				_lowerCamelCase:		float,					_lowerCamelCase:		float			)					->						float:

	'''simple docstring'''




	if temperature < 0:
		raise Exception('''Temperature cannot be less than 0 K'''			)
	if molar_mass <= 0:
		raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol'''			)
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
					import doctest

					# run doctest
					doctest.testmod()

					# example
					UpperCAmelCase        =				3_0_0
					UpperCAmelCase        =				2_8
					UpperCAmelCase        =				rms_speed_of_molecule(temperature, molar_mass)
					print(f"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")


